As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily relt on two prior assumption: low-rank background and sparse anomaly, along with additional spatial assumptions of the background. However, most methods only utilize the sparsity prior assumption for anomalies and rarely expand on this hypothesis. From observations of hyperspectral images, we find that anomalous pixels exhibit certain spatial distribution characteristics: they often manifest as small, clustered groups in space, which we refer to as cluster sparsity of anomalies. Then, we combined the cluster sparsity prior with the classical GoDec algorithm, incorporating the cluster sparsity prior into the S-step of GoDec. This resulted in a new hyperspectral anomaly detection method, which we called Turbo-GoDec. In this approach, we modeled the cluster sparsity prior of anomalies using a Markov random field and computed the marginal probabilities of anomalies through message passing on a factor graph. Locations with high anomalous probabilities were treated as the sparse component in the Turbo-GoDec. Experiments are conducted on three real hyperspectral image (HSI) datasets which demonstrate the superior performance of the proposed Turbo-GoDec method in detecting small-size anomalies comparing with the vanilla GoDec (LSMAD) and state-of-the-art anomaly detection methods.

This paper proposes the integration of the Multiple Signal Classification (MUSIC) algorithm with Affine Frequency Division Multiplexing (AFDM)-based Integrated Sensing and Communications (ISAC) system, enabling high-accuracy joint range-velocity estimation for multiple targets. To address the high computational complexity of conventional MUSIC, we propose a low-complexity iterative MUSIC (LCI-MUSIC) variant that employs cyclic spectral peak search instead of uniform grid scanning. This innovation achieves an exponential reduction in computational complexity while maintaining detection accuracy. In addition, the LCI-MUSIC enables trade-offs between complexity and estimation accuracy. Simulations demonstrate that compared to conventional MUSIC, the LCI-MUSIC algorithm achieves higher estimation accuracy at the same computational complexity.

Self-supervised learning has made significant strides in hyperspectral image classification. For contrastive learning, traditional data augmentation methods will lead to biases with real-world spectra, limited variability, loss of information. To address those limitations, this study introduces diffusion models to generate realistic synthetic samples as views input to contrastive learning. This diffusion-based augmentation enables our contrastive learning network to learn more robust spectral-spatial representations. Experimental results on two publicly available datasets indicate that this approach outperforms numerous existing methods. And the ablation study demonstrates the effectiveness of employing diffusion models.

In recent years, self-supervised learning has made significant strides in hyperspectral image classification [1]. However, different approaches come with distinct strengths and limitations. Contrastive learning excels at extracting key information from large volumes of redundant data, but its training objective can inadvertently increase intra-class feature distance. To address this limitation, we leverage diffusion models for their proven ability to refine and aggregate features by modeling complex data distributions. Specifically, diffusion models’ inherent denoising and generative process are theoretically well-suited to enhance intra-class compactness by learning to reconstruct clean, representative features from perturbed inputs. We propose the new method - ContrastDM. This approach generates synthetic features, improving and enriching feature representation, and partially addressing the issue of sample sparsity. Classification experiments on three publicly available datasets demonstrate that ContrastDM significantly outperforms state-of-the-art methods.

As a low-rank regularization technique, the tensor nuclear norm (TNN) has been extensively employed for hyperspectral anomaly detection (HAD). However, traditional TNN and its variants often suffer from rank estimation bias and are difficult to effectively capture the spectral-spatial correlation of complex backgrounds. In addition, the extracted sparse component is often submerged in the background, resulting in abnormal targets being insignificant. To overcome these problems, this letter proposes an enhanced low-rank and joint saliency prior (ELRJSP) method for HAD. Specifically, within the framework of tensor singular value decomposition (t-SVD), we propose an improved weighted tensor nuclear norm (IWTNN), defined as a weighted combination of the weighted tensor nuclear norm (WTNN) and the weighted nuclear norm of the core matrix. This novel norm effectively integrates information from both the original background tensor and its core tensor, thereby leveraging spectral-spatial structural characteristics more comprehensively and leading to enhanced accuracy in background estimation. Furthermore, in order to accurately detect and highlight abnormal targets, under the constraint of the tensor ℓF,1-norm, a visual saliency sparse weight tensor to constrain the abnormal tensor is designed by combining the visual saliency weight graph and the sparse optimized weight graph and extending them to the tensor domain. Comparative experiments on three real hyperspectral datasets demonstrate that the developed ELRJSP algorithm outperforms several advanced algorithms.

Enhancing classification accuracy in hyperspectral image classification under small sample scenarios remains a critical challenge. This study introduces a novel framework based on Conditional Denoising Diffusion Implicit Models (DDIM), featuring an adaptive synthetic sample generation strategy and label noise training. Our approach dynamically allocates synthetic samples for each class based on classification difficulty and generation reliability, while label noise training enhances the model's generalization capabilities. As a flexible data augmentation method, our framework can be seamlessly integrated with various feature extraction techniques, demonstrating significant potential for further performance improvements. Validated on the Salinas and Pavia University datasets, our method highlights the effectiveness of diffusion models as a robust tool for addressing data scarcity, paving the way for broader applications in hyperspectral image classification and remote sensing.

In recent years, researchers have focused on mitigating the impact of spectral variability (SV) on unmixing performance, leading to the development of various deep-learning-based unmixing networks. Currently, most unmixing networks that account for SV mainly rely on probabilistic generative models, which lacks specific constraints for SV and suffers from the instability of solutions generated by probabilistic models. This results in the generated endmembers or SV components lacking clear physical meaning. To avoid the problems above, we propose an endmember-oriented Transformer network (EOT-Net) that leverages the advantages of endmember bundles to introduce variability while providing stable endmember results with clear physical meaning. We design an endmember-oriented Transformer (EOT) to capture endmember-specific features through directional subspace projection and a low-redundancy attention (LRA) mechanism. Subsequently, the proposed network is divided into two branches: endmember generation and abundance estimation, to process endmember-specific features. In the endmember generation branch, endmember-specific features are transformed into intraclass weights that are used to combine signatures within the bundles, and a set of endmembers is generated for each pixel. In the abundance estimation branch, endmember-specific features are integrated using a heterogeneous information fusion (HIF) module that leverages the spatial distribution heterogeneity of the endmembers, ultimately producing the abundance results. We applied the proposed algorithm to both synthetic and real datasets, and the experimental results demonstrated the model’s superiority.

With the advantages of deep learning (DL) techniques, various infrared (IR) small target detection networks have been proposed. While many networks aim at single-frame detection through supervised learning, ignoring abundant spatial-temporal information and causing heavy labeling costs. In this paper, we develop a 3-D spatial-temporal knowledge aware-based unsupervised network for IR target detection (STUTD). Specifically, we transform IR sequences into 3-D spatial-temporal tensors as data foundation. Based on the designed spatial-temporal Swin Transformer block (ST-STB), we introduce a multiscale feature extraction and aggregation (MFEA) module for effective feature extraction. And a Variational Autoencoder (VAE)-style background reconstruction module with a multihead gating mechanism is designed for background reconstruction. Besides, a designed sparse cardinality selection of residuals performs element-wise filtering on the residuals between the original tensors and the reconstructed background to obtain a pure target tensor. By an unsupervised learning approach, STUTD can achieve IR small target detection. Comprehensive experiments illustrate the superiority of STUTD among state-of-the-art methods. It can be concluded that STUTD has satisfactory overall performance and real-time performance.

The need for precise and efficient monitoring of the Earth’s surface has become increasingly urgent. However, existing methods primarily rely on Siamese-based neural network, which both restrict the extraction of change information and reduce computational efficiency. To address these issues, we propose a One-Stream neural network (OSMT) based on Multi-level feature Fusion for hyperspectral image (HSI) change detection (CD). The proposed method leverages the multi-level semantic change features of the bi-temporal images. First, a band-wise image fusion strategy is utilized to obtain the fused difference image of the bi-temporal HSIs. Then, a residual connection block with spatial-spectral attention mechanism is employed to extract multi-level change features. Next, a multi-level feature fusion module composed of Transformer encoders is used to fuse the change features of different levels, including spatial details at the lower level and semantic information at the higher level. Finally, a classifier is employed to predict the detection results. Experimental results on one public dataset validate the effectiveness of our proposed method.

Noise corruption commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral un-mixing algorithms. The noise formulation of HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study formulates a close-to-reality noise model and proposes a learning-based hyperspectral image denoising method for hyperspectral un-mixing. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively.

Hyperspectral unmixing is a crucial step in hyperspectral image processing. Hyperspectral images in real scenes are saturated with spectral variability, and unmixing performance is limited. We propose the Spectral Variability Attention Net (SVA-Net). We have separately designed a Complementary Feature Enhancement Module (CFE) and a Spectral Variability Attention Mechanism to capture both the original material features in the image and other easily overlooked features. In addition, we design the improved mixing model based on augmented linear mixing model (ALMM) to better cope with the effects of spectral variability. Experiments on real datasets demonstrate the effectiveness of our model.

Recently, convolution neural networks (CNNs) have flourished in hyperspectral image (HSI) change detection (CD). However, these approaches typically rely on single-scale and single-level features to obtain change information, limiting the further boost detection accuracy. To solve the above problem, we propose a novel multiscale and multilevel spatial–spectral features aggregated neural network ($\text{M}^{2}\text{S}^{2}$Net) for HSI CD. The proposed method leverages the multiple spatial–spectral (SS) features of bi-temporal images to mine temporal change information. First, the HSIs are cropped into patches with different spatial and spectral sizes to provide various scales SS information and alleviate the problem of spectral redundancy. Then, the patches are utilized to capture the multiscale SS features in the residual connection block (ResBlock). These bi-temporal features are inputted into the Transformer encoder-based feature fusion module to learn the discriminative change representations. Via the global receive field of the self-attention mechanism, various fine-grained change information is learned from the features at different scales. Finally, the change features of each level are aggregated to produce the change map. The experiments on two public HSI datasets verify the effectiveness of the proposed method. Specifically, the overall accuracy (OA) of the$\text{M}^{2}\text{S}^{2}$Net surpasses the second-best method by 0.56% and 0.10% on the Farmland and River datasets, respectively.

Models based on low-rank and sparse decomposition (LRaSD) have been rapidly developed in the hyperspectral anomaly detection (HAD) task. However, traditional LRaSD models usually impose multiple complex regularizers on the background components, which inevitably increases the computational cost and fails to maximize the effectiveness between them. In addition, regularizers for abnormal components mostly penalize each pixel with the same intensity. To tackle these challenges, we propose a model based on enhanced 3-D total variation (TV) and sparse reweighted regularization, referred to as E-3DTVSR. Specifically, an enhanced 3-D TV (E-3DTV) regularization is adopted to simultaneously characterize the low-rank and piecewise smoothness of the background. Since E-3DTV applies sparse regularization to subspace base maps of gradient maps along all bands of a hyperspectral image (HSI), rather than the gradient maps themselves, this effectively removes noise and improves the detection efficiency of the model. Meanwhile, in order to enhance the sparsity of abnormal targets and distinguish sparse nonabnormal pixels, combined with the log-sum function and the reweighted$\ell _{1}$minimization strategy, a sparse reweighted regularization is designed to adaptively assign weight to each target. Experiments demonstrate that E-3DTVSR reaches the area under the ROC curve (AUC) scores of 99.86%, 99.59%, and 99.72% on three public HSI datasets, respectively, outperforming the current advanced approaches.

Self-supervised learning effectively leverages the information from unlabeled data to extract spatial-spectral features that are both representative and discriminative, partially addressing the challenge of high data annotation costs in hyperspectral image classification. Inspired by the success of redundancy reduction-based self-supervised learning in other domains, we introduce it into HSIC. We proposed a spatial-spectral feature extraction network, HyperBT, to more effectively reduce redundancy. Specifically, we added the off-diagonal terms of the cross-covariance matrix to the loss function and new data augmentation methods, including band bisection and edge weakening. Experimental results demonstrate that our method achieves high accuracy in classification, surpassing many state-of-the-art methods. Through ablation experiments, we validate the effectiveness of each component in the loss function.

Hyperspectral image change detection (HSI CD) refers to identifying and analyzing differences between two HSIs acquired in the same area but at different times. However, current deep learning (DL)-based methods have limitations in fully exploiting the change information between bitemporal images and utilizing multilevel features. To address these issues, we propose a novel Multi-level features Fused and Change information Enhanced neural Network (MFCEN) for HSI CD. The proposed MFCEN method leverages the hierarchical low- and high-level features of bitemporal images, allowing for the direct capture and enhancement of change features. First, a Siamese-based network is employed to extract multilevel features from the bitemporal images, including low-level spatial details and high-level semantic features. Within the temporal change information branch (TCIB) at each level, the change features are reinforced by the semantic features of each image, and the change features act as a guiding force to direct the feature extraction of each bitemporal image to focus more on the change region. Next, the enhanced change features of each level are fed into the multilevel features fusion module (MFFM) to aggregate the fine-grained details and high-level semantics. Finally, the fused features, enriched with multilevel change information, are utilized for CD. The experiments demonstrate that our proposed MFCEN outperforms existing methods on three public datasets. The code will be available athttps://github.com/Ding201901/MFCEN.

Infrared (IR) small target detection plays an essential role in many civilian and military fields. And low-rank and sparse decomposition-based detection techniques have gradually shown superiority. However, there are two crucial issues. The first is accurate low-rank background estimation, the second is effective target enhancement and background suppression. To address the abovementioned issues, we propose a method named feedback spatial-temporal infrared small target detection with framelet- and Log-based improved tensor nuclear norm (FST-FLNN). Specifically, we transform the current frame to be detected and its adjacent frames into a spatial-temporal tensorD, followed by projectingDonto the principal component analysis (PCA)-driven orthogonal complement subspace to achieveD⊥VPCAmfor preliminarily eliminating principal background components. To further enhance the low-rank property of residual background components and estimate it more accurately, we fully utilize the spatial-temporal information of the background and the corresponding core to improve tensor nuclear norm using Log operation in framelet domain, which is called framelet-and Log-based improved tensor nuclear norm (FL-ITNN). In addition, we introduce a posterior knowledge extraction method based on extended 3-D morphology as feedback mechanisms towards the model. According to the different feedback objects of posteriors, we provide two different feedback mechanisms and their corresponding LRSD-based target detection models FST-FLNN. Finally, two efficient ADMM-based solving frameworks are designed. Compared with nine state-of-the-art competitive methods, comprehensive qualitative and quantitative experiments and analysis on five real complex IR sequences illustrate the satisfactory target detectability (TD), background suppressibility (BS) and overall performance of the two versions of FST-FLNN.

Infrared (IR) small target detection is a crucial aspect in both military and civilian fields. Due to the poor quality and complex scenarios, the existing technologies lack superior real-time and overall detection performance. Tensor analysis has shown superiority, but there are three key issues. The first pertains to appropriate tensor-structured data, the second concerns a more comprehensive tensor decomposition architecture, and the third is to achieve more satisfactory real-time performance. This article proposes an IR small target detection method named 4-D spatial–temporal tensor decomposition with block term decomposition-based norm and multidirectional derivative-based priors (4DST-BTMD). It converts the target detection task into a low-rank and sparse decomposition (LRSD) optimization problem of decomposing background, target, and noise tensors from 4-D spatial–temporal domain. First, we construct a 4-D spatial–temporal tensor, followed by projecting it into a constructed data-sphered space, which lays the data foundation for decomposition. Then, based on the 4-D sphered tensor, a low-rank surrogate named block term decomposition-based norm (BTDN) for background estimation is proposed, which fully integrates global information across different dimensions. Meanwhile, more effective salient 4-D prior tensors based on multidirectional derivatives are designed to effectively guide the model to focus on significant areas. Finally, an efficient ADMM-based solving framework is designed for the LRSD model. Furthermore, with seven state-of-the-art competitive methods, extensive experiments and analysis illustrate that the proposed 4DST-BTMD not only demonstrates the superiority in background suppressibility (BS) and target detectability (TD) but also has satisfactory real-time detection performance.

In various civilian and military applications, infrared (IR) small target detection is faced with difficulties because of complex backgrounds, low signal-to-clutter ratio, and clutter interference. Although low-rank and sparse theories based on tensor analysis have been widely employed, three crucial issues persist. The first concerns accurate estimation of target and background, the second involves the development of a more comprehensive target detection model, and the last one is real-time detection performance. This article proposes an IR small target detection method named clustering and tracking-guided infrared small target spatial-temporal prediction completion model (CTSTC). Specifically, a 3-D spatial-temporal tensor is constructed in the high-frequency domain, incorporating target detection priors and subsequent yet-to-be-detected IR frames as foundational data for the prediction completion model. Secondly, we improve K-means clustering algorithm for multiple low-rank background clusters, followed by designing an IKC-derived rank surrogate, resulting in more accurate low-rank background estimation. Furthermore, a Bayesian-inference-derived tracking regularization is incorporated into the completion model to describe the motions and state transitions of targets, thereby improving the robustness of interferences. Meanwhile, an efficient ADMM-based optimization scheme is designed for solving the completion model. Additionally, spatial-temporal evolution-based fusion strategies are proposed, which utilize the past and future spatial-temporal knowledge for the assistance of the detection of current frames and enhance both TD and BS of the prediction completion model. Compared with ten state-of-the-art competitive methods, extensive experiments on five practical datasets have illustrated the superiority of CTSTC in terms of target detectability (TD), background suppressibility (BS), and overall performance.

As an important task in hyperspectral image (HSI) processing, hyperspectral anomaly detection has gained increasing attention and has been extensively studied. However, most existing methods mostly focus on the statistics spectral distribution of pixels and the spatial correlation of pixels, while ignoring pixels’ correlation in the spectral space. Compared with statistics spectral distribution, the spectral correlation represents not only the spectral distribution but also the relations between different pixels in the spectral space. In this article, we propose a novel graph-based hyperspectral anomaly detection method which exploits the correlation of pixels in spectral space. We model the spectral correlation through the structure of the graph which is constructed by a KNN-like strategy. Meanwhile, edge modification (EM) operations including the edge adding (EA) and edge deleting (ED) operations are adopted to further optimize the graph structure. Once the graph structure is determined, the vertices with fewer edges connected will be masked iteratively. This is because vertices with fewer connections have a higher probability of being anomalous compared to others. Then, all the masked vertices will be reconstructed using an iterative graph propagation updating strategy. Since background pixels exhibit higher correlation, they can be effectively reconstructed through graph propagation. However, anomalies are more challenging to reconstruct due to their low spectral correlation. Experiments are conducted on four real HSI datasets which demonstrate the superior performance of the proposed iterative graph propagation anomaly detector (IGPAD) method in detecting small-size anomalies compared with other graph-related and state-of-the-art anomaly detection methods.

With the advantages of being scalable, labeled-sample free, and structurally simple, autoencoder (AE) is suitable for hyperspectral target detection (HTD) with discriminative attributes extraction ability. However, there are some bottlenecks in current AE-based approaches, like network complexity, parameter determination, and dictionary reliability problems. In this article, a novel target-driven AE (TAE) with a constrained latent code layer and improved loss function is proposed. To achieve effective noise and interference annihilation, a simple AE-based network is designed with the adaptively determined number of hidden neurons by estimating the ranks of the background (BKG) and the target component. In addition, to untangle the dependence on the BKG dictionary, a novel strategy is imposed on the traditional Kullback–Leibler (KL) divergence, called the truncated KL divergence (TKL), is designed and imposed by the loss function of TAE, guiding the propensity for network reconstruction of the targets over BKG with probabilities rather than dictionaries. Furthermore, unlike AE-based methods with forward strategy, a novel target-driven iterative AE (ITAE) framework is developed to further exploit the potential of prior and posterior knowledge. The iterative system repeatedly trains the same TAE and adds the weighted constrained energy minimization (CEM) detection map back to the current data matrix for the next iteration. ITAE provides a general classic-deep learning collaborative framework that can train any AE with a traditional detector through an iterative process to improve the diversity between the targets and the BKG. To validate the effectiveness of ITAE, six other state-of-the-art HTD methods are chosen for comparison under six datasets of various scenarios. Based on the 3-D receiver operating characteristic (ROC) curve-derived evaluation metric, we conducted experiments for parameter analysis, detection performance comparison, noisy study, and ablation study. Results show that ITAE outperforms other methods, especially in target detectability.

The infrared small target detection (IRSTD) task presents significant challenges due to low signal-to-clutter ratio, complicated background, and strong interferences. While tensor theory has shown promise in detection performance, three issues regarding damaged tensor construction, inaccurate tensor models, and high computation complexity remain. This study addresses these issues by introducing an independent spatial-temporal perspective, and proposes a fast and separable spatial-temporal tensor completion model. A new tensor structure named separable spatial-temporal patch-tensor pair (SSPP) is conceived to alleviate the dilemma of maintaining neighborhood structure and temporal consistency when constructing image tensors. By treating spatial and temporal dimensions as independent, SSPP enables flexible distribution hypotheses and representations in each dimension. Two tensor models are devised: the spatial model focuses on target enhancement in the spatial dimension, while the temporal one concentrates on suppressing strong interference in the temporal dimension. A long-term memory regularization is further introduced to the temporal model for target movement perception, enhancing its robustness to interferences. By combining these tensor models and employing a coarse-to-fine detection strategy, our method offers an effective solution for IRSTD. Extensive experiments on practical datasets have demonstrated the superiority of the proposed method in terms of target enhancement, background suppression, and detection efficiency.

Hyperspectral images (HSIs) encounter an inherent challenge due to spectral variability (SV), which directly impacts the accuracy of endmember extraction and abundance estimation. Most unmixing networks based on autoencoder (AE) overlook the impact of SV and instead focus more on exploring the features of endmembers. In this article, we propose a feedback information-guided SV attention network (FSVA-Net), an AE-based neural network that utilizes a feedback information-guided structure to exploit and model the SV factors. We develop a feedback enhancement module (FEM) that utilizes residuals from a linearly reconstructed image to reweight and emphasize pixels affected by SV. And an SV attention (SVA) mechanism is proposed to explore interference-affected information in a global perspective according to the enhanced feature. With the extracted SV-related feature, we design a generative augmented linear mixing model (ALMM)-based decoder to model SV from the perspective of scaling and perturbation in a more reliable way. In a more concern on SV architecture, the proposed network achieves excellent performance on both synthetic and real datasets.

Autoencoders (AEs) have demonstrated excellent performance in the field of hyperspectral unmixing (SU), due to their self-supervised nature and ease of implementation. Recently proposed AE-based networks contend that local spatial information limits further improvement in unmixing accuracy and tends to explore and utilize global information, which improves unmixing accuracy at the expense of increased computational complexity. However, we believe that precise unmixing can be achieved by fully leveraging local information. In this article, we propose a unidirectional local-attention AE network (ULA-Net) that explores spatial information pixel by pixel and achieves accurate spatial–spectral feature fusion. ULA-Net utilizes unidirectional local attention (ULA) module to calculate the correlation between neighboring pixels and the central pixel within local regions, extracting discriminative local information. Moreover, ULA-Net effectively extracts relevant spatial information and suppresses irrelevant information based on a double fusion strategy (DFS) module. This process achieves more accurate control over the contribution of spatial information by implementing information fusion in both the pixel and feature dimensions. To address spectral variability, we implement the extended linear mixing model (ELMM) in the decoder part to improve unmixing accuracy without increasing the number of parameters. We conduct ablation experiments to investigate the roles of each module. Experimental results on both synthetic and real datasets demonstrate the effectiveness of the proposed network.

In recent years, tensor representation (TR) based hyperspectral anomaly detection approaches have attracted more and more attention. However, two urgent issues still need to be addressed: 1) existing tensor decomposition approaches for hyperspectral anomaly detection (HAD) cannot make full use of the spectral-spatial correlation of background components in hyperspectral images (HSIs); 2) most approaches based on TR overlook the piecewise-smooth of background components that exist simultaneously in the spectral and spatial domains. To this end, with the aid of an advanced multi-scale entanglement renormalization ansatz (MERA) tensor network, this paper proposes an algorithm based on MERA decomposition and enhanced total variation regularization (MERAETV) for HAD. Specifically, MERA decomposes the background tensor by contracting a top-level factor with the remaining semi-orthogonal and orthogonal factors. Due to the intricate interplay between semi-orthogonal (low-rank) and orthogonal factors, low-rank MERA approximation exhibits a robust representational capacity that effectively captures the spectral-spatial correlation of the background component. Meanwhile, an enhanced total variation (ETV) regularization is devised to capture the inherent piecewise-smooth of the background component in both spectral and spatial domains. Furthermore, our algorithm incorporates group sparsity constraint and Gaussian noise term to enhance the discrimination between anomalies and background. Finally, a highly efficient update scheme based on the alternating direction method of multipliers (ADMM) is designed. A large number of experiments on one synthetic and seven real HSIs demonstrate the superiority of our proposed approach.

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services’ large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services’ current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services’ runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services’ historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services’ historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services’ runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

Due to the poor quality image with low signal-to-clutter ratio (SCR), infrared (IR) small target detection is faced with great challenges in the remote sensing field. Despite the fact that the local contrast measure (LCM) has been widely applied for IR target detection, the existing LCM-based methods suffer from weak target detectability (TD) or background suppressibility (BS) in complicate background. In this paper, we propose a novel IR small target detection method based on improved tri-layer window local contrast measure (TrLCM). With an additional isolation circle in TrLCM, the influence of background on target detection is reduced to a certain extent. Besides, background suppressibility is also promoted through a designed adaptive adjustment coefficient. Comprehensive experiments and analysis on three datasets verify that the proposed TrLCM achieves advanced TD, BS and overall performance.

The common purpose of HTD is to distinguish the target from the BKG in HSIs using either fully-known or partly-known prior knowledge. Traditional HTD methods generally rely on distance or similarity measurement and statistical techniques to identify discriminating characteristics. Inspired by the simplicity and efficiency of AE in feature extraction and dimension reduction, and considering the susceptibility of limited prior knowledge to spectral variability, a novel HTD method based on hidden dimension-restricted iterative AE guided by posterior knowledge is proposed. The latent space mapping from the original data is constrained with the concept of VD by the algorithm of NWHFC, which allows the reconstruction more tendentious to target and purified BKG. The posterior knowledge, obtained by CEM on a subpixel level, is iteratively added to the input of AE to improve the diversity of the target and BKG collaboratively. The results show that the BKG is effectively suppressed and the target is prominently highlighted layer by layer. Comprehensive experiments and analysis conducted on the public dataset demonstrate that the proposed method is structurally simple yet efficient, outperforming five state-of-the-art algorithms rank by 3-D ROC. The key indicator, target detectability, exceeds the comparison methods by 51.1%.

Some existing anomaly detection methods convert a 3-D hyperspectral data cube into a 2-D matrix, which inevitably destroys the spatial-spectral structure information of the hyperspectral data, resulting in the degradation of detection performance. In this paper, we propose a tensor low-rank sparse representation learning (TLRAD) method for hyperspectral anomaly detection (HAD), which can effectively maintain the spatial-spectral structure of raw hyperspectral data. Specifically, based on tensor low-rank representation (TLRR) learning, both low-rank constraints and sparsity constraints are simultaneously imposed on the coefficient tensor to capture the global and local spatial-spectral structure information of hyperspectral image (HSI), respectively. For anomaly tensor, the tensor ℓ21-norm is applied to encourage the group sparsity of anomalous pixels. Furthermore, the tensor robust principal component analysis (TRPCA) approach is utilized to construct a robust background dictionary tensor. Experimental results gained employing two real hyperspectral datasets prove the superiority of the proposed approach compared to some state-of-the-art algorithms.

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial-spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation learning (ETLR) model is proposed for HAD. Specifically, the original 3-D hyperspectral image (HSI) data is firstly decomposed into a structural background component, an anomaly component and a noise component. Among them, with the help of multi-subspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an ℓ2,1,1-norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensorF-norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior than current HAD algorithms.

Crowds often appear in surveillance videos in public places, from which anomaly detection is of great importance to public safety. Since the abnormal cases are rare, variable and unpredictable, autoencoders with encoder and decoder structures using only normal samples have become a hot topic among various approaches for anomaly detection. However, since autoencoders have excessive generalization ability, they can sometimes still reconstruct abnormal cases very well. Recently, some researchers construct memory modules under normal conditions and use these normal memory items to reconstruct test samples during inference to increase the reconstruction errors for anomalies. However, in practice, the errors of reconstructing normal samples with the memory items often increase as well, which makes it still difficult to distinguish between normal and abnormal cases. In addition, the memory-based autoencoder is usually available only in the specific scene where the memory module is constructed and almost loses the prospect of cross-scene applications. We mitigate the overgeneralization of autoencoders from a different perspective, namely, by reducing the prediction errors for normal cases rather than increasing the prediction errors for abnormal cases. To this end, we propose an autoencoder based on hybrid attention and motion constraint for anomaly detection. The hybrid attention includes the channel attention used in the encoding process and spatial attention added to the skip connection between the encoder and decoder. The hybrid attention is introduced to reduce the weight of the feature channels and regions representing the background in the feature matrix, which makes the autoencoder features more focused on optimizing the representation of the normal targets during training. Furthermore, we introduce motion constraint to improve the autoencoder’s ability to predict normal activities in crowded scenes. We conduct experiments on real-world surveillance videos, UCSD, CUHK Avenue, and ShanghaiTech datasets. The experimental results indicate that the prediction errors of the proposed method for frequent normal crowd activities are smaller than those of other approaches, which increases the gap between the prediction errors for normal frames and the prediction errors for abnormal frames. In addition, the proposed method does not depend on a specific scene. Therefore, it balances good anomaly detection performance and strong cross-scene capability.

Recently, hyperspectral anomaly detection (HAD) methods based on tensor low-rank representation (TLRR) have received widespread attention. However, most of them tend to emphasize the utilization of multiple types of prior knowledge to characterize background components, while the prior information about anomaly components is limited. Additionally, the constructed background dictionary is also susceptible to noise and outliers. To address these challenges, this paper focuses on both the background and abnormal components, proposing a robust tensor low-rank sparse representation with saliency prior (RTLSR-SP) method for HAD. Specifically, for the background component described by the dictionary tensor and the corresponding coefficient tensor, tensor nuclear norm (TNN) constraint and sparsity constraint are imposed on the coefficient tensor simultaneously to capture the global and local spatial-spectral structure information of the hyperspectral image (HSI), respectively. For the anomalous component, we design a sparse saliency prior weight tensor to enhance the saliency of anomalous targets. Meanwhile, the tensor ℓF,1-norm is also integrated into the model to better separate abnormal targets from the background. Furthermore, combining tensor robust principal component analysis (TRPCA) and skinny tensor singular value decomposition (skinny t-SVD), a robust background dictionary is constructed. Finally, an efficient iterative algorithm based on the alternating direction method of multipliers (ADMM) is derived to optimize the RTLSR-SP model. Comprehensive experimental findings on one simulated dataset and six real hyperspectral datasets demonstrate the effectiveness and superiority of the proposed algorithm compared with eight state-of-the-art HAD algorithms.

To meet the rapidly increasing need of computation-intensive and latency-sensitive applications, mobile edge computing (MEC) has attracted tremendous attention from both academia and industry. However, the runtime reliability of edge services fluctuates over time due to the dynamics in their internal states and the external environment. This causes the distribution of edge services’ reliability data streams to vary in the form of concept drift. Severe negative reliability drifts indicate that an edge service may be suffering from a performance anomaly or a runtime failure. To ensure the stable operation of edge services, we propose A-Detection, a concept drift-based runtime reliability anomaly detection approach for edge services adaptation. We integrate reservoir sampling and singular value decomposition (SVD) for large-scale streaming data sampling and feature extraction. Jensen Shannon (JS) divergence is utilized to develop a dissimilarity metric of data stream distribution, called FDC, for runtime edge service reliability anomaly detection. When an anomaly is detected in a running edge service, checkpoint-retry is combined with computation offloading to implement runtime reliability adaptation. Extensive experimental results verify and demonstrate the effectiveness and efficiency of A-Detection.

Low rank and sparse representation (LRaSR)-based approaches have been widely used for hyperspectral anomaly detection (HAD) by effectively decomposing a hyperspectral data set into a low - rank component for background (BKG), a sparse component for anomalies, and a residual component for noise. This paper proposed a novel hyperspectral anomaly detection (AD) method based on data sphering (DS) and unsupervised target detection with sparse cardinality (DS-UTSSC). First of all, DS-UTS uses data sphering to remove BKG, which is characterized by 1 st and 2ndstatistics, for original data$X$. Second, potential anomaly component$\mathrm{S}_{\text{DS}-\mathrm{U}\text{TS}}$is generated via unsupervised target detection and subspace projection for the sphered data$X$. To further reduce the impact of noise on anomaly detection, sparse cardinality (SC) is incorporated to obtain$\mathrm{S}_{\text{DS}-\text{UTSSC}}$. Finally, RX-AD is implemented on$\mathrm{S}_{\mathrm{D}\text{S-UTSSC}}$to detect anomalies. The experimental results validate that DS-UTSSC is very competitive against the LRaSR-based models and AE-based method.

Infrared small target detection (IRSTD) is a challenging task in the remote sensing field, due to the low signal-to-clutter ratio (SCR) and intricate background. Although local contrast measure (LCM) has been widely applied for IRSTD, the existing LCM-based methods suffer from high sensitiveness to heavy clutters. To resolve this problem, this paper proposes a novel LCM named omnidirectional mirror gradient dissimilarity measure (O-MGDM) for small target enhancement. A concept of mirror gradient dissimilarity (MGD) is first presented to evaluate the pixel gradient inconsistency of opposite directions. It is analyzed that small target pixels exhibit considerable MGD in all directions, while heavy clutters hold large MGD in certain directions. Then, the novel O-MGDM is proposed by uniting the omnidirectional MGDs, which achieves target enhancement and heavy clutter suppression simultaneously. After that, an adaptive threshold is used to segment the targets readily. Extensive experimental results on practical data sets demonstrate that the proposed O-MGDM achieves advanced detection performance.

The router throttling mechanism provides us a chance to prevent DDoS attack proactively through rate-limiting suspicious traffic before effective detection mechanism. The existing search-based and learning-based studies are highly customized to server load and can hardly cope with the constantly changing server load and unseen scenarios. To address the problem above, we design a self-evolutionary DDoS defense system, DeepThrottle, based on deep reinforcement learning (DRL) and router throttling mechanism in software defined network (SDN). The experimental results demonstrate that the DeepThrottle improves the passing ratio of normal traffic to the victim server, and reduces the server load under unseen attack scenarios compared with the state-of-the-art RL-based method.

Summary With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform. Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self‐description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD‐APIR, a singular value decomposition (SVD)‐based behavior‐aware cloud service APIs recommendation approach for large‐scale software cloud directory platforms. In SVD‐APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS‐Dream dataset demonstrate the effectiveness and efficiency of the proposed approach.

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is essential for infrared search and tracking systems. In this letter, we establish a multiple small targets detection method derived from hierarchical maximal entropy random walk (HMERW). The HMERW revolves the limitation of strong bias to the most salient target of the primal maximal entropy random walk (MERW) based on a proposed graph decomposition theory. To enhance the characteristics of small targets and suppress strong clutters, we design a specific weight matrix for HMERW instead of using the conventional weight matrix in MERW. First, a stationary distribution map is obtained by importing the filtered infrared image into the HMERW. Second, a coefficient map is constructed based on the designed weight matrix to fuse the stationary distribution map. Then, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical datasets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression, and multiple small targets detection.

Although remote sensing image registration has been studied for several years, achieving accurate image registration remains a challenging task due to the complicated conditions surrounding remote sensing images. To improve the accuracy and robustness of image registration, we proposed a registration method based on the global mixed structure similarity (GMSIM) measure. This measure mixes the structure similarity in both the frequency domain and the intensity domain because phase-based structure similarity in the frequency domain is sensitive to intensity contrast and spatial translation, and gray-based structure similarity in the intensity domain is efficient to structure change. Feature-based registration methods are used to generate the initial registration parameters. After that, we calculate the final registration parameters by maximizing GMSIM. Quantum-behaved particle swarm optimization (QPSO) is utilized to solve the optimal results of GMSIM due to its high efficiency. The proposed method has been evaluated on several remote sensing images differing in scale, gray, and scene and compared with three state-of-the-art registration methods. Experimental results demonstrate the high accuracy of the proposed scheme.

Low-rank and sparse representation (LRaSR)-based approaches have been widely used for anomaly detection (AD). Their central ideas are to minimize the rank of the low-rank space constrained to predetermined values, while using various regularization parameters to control the sparse representation. Three key issues arise from LRaSR. The first is how to determine the constrained rank. The second is an appropriate selection of regularization parameters. The third one is the detector used for AD. This article presents a new but rather simple competing model, called component decomposition analysis (CDA) which represents a data space X as a linear orthogonal decomposition of three components, X = PC$^{{m}} +$IC$^{{j}} +$N with${m}$principal components, PC$^{{m}}$, generated by principal component analysis (PCA) and${j}$independent components, IC$^{{j}}$, generated by independent component analysis (ICA) plus a noise component N. CDA offers several advantages over LRaSR. First, CDA uses well-known component analysis techniques to decompose the dataset without solving constrained optimization problems. Second, the values of${m}$and${j}$can be automatically determined by virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD). To better extract anomalies from the IC$^{{j}}$component space, the concept of sparsity cardinality (SC) is further incorporated into CDA to derive a CDASC anomaly detector (CDASC-AD). The experimental results demonstrate that CDASC-AD is very competitive against the LRaSR-based models and performs well in hyperspectral AD.

Despite that many state-of-the-art methods have been proposed for infrared (IR) small target detection, target detectability (TD) and background suppressibility (BS) cannot be significantly improved simultaneously, especially in complex situations. This article proposes a novel IR small target detection method named improved multimode nuclear norm joint local weighted entropy contrast (IMNN-LWEC), which represents the IR target detection task as an optimization problem for tensor decomposition of three components in the spatial–temporal domain, including background tensor, target tensor, and sparse structure tensor. First, to utilize the spatial and temporal information in an IR sequence effectively, we transform the original IR sequence into a nonoverlapping spatial–temporal patch tensor. Second, a nonconvex approximation of tensor rank called improved multimode weighted tensor nuclear norm (IMWTNN) is proposed to estimate background tensor rank, which is of benefit to separate the background component more completely from the original image. Third, based on the structure tensor theory, we introduce a new sparse prior map called LWEC via a designed image entropy operator and a new prior information filter, which can further preserve the target and suppress the background simultaneously. Besides, a novel tubewise sparse regularization term is designed to identify linear sparse structures. The Frobenius norm is used to characterize noise. Finally, to solve the proposed model, an efficient optimization scheme utilizing the alternating direction method of multipliers (ADMM) is designed to retrieve the small targets. Comprehensive experiments on five datasets witness the superior TD and BS performance of the proposed method compared with nine state-of-the-art detection methods.

Infrared small target detection (IRSTD) is a challenging task, due to the scarce target feature, complex background interferences, and poor image quality. The existing studies handle the IRSTD problem by making specific distribution assumptions of target and background, which incurs two issues. First, the specific assumptions of background cannot always hold. Second, the distorted target distributions contaminated by heavy clutters and noise are difficult to model statically. This paper handles the problems from the discriminative and dynamic perspectives, and proposes an original mechanism named dynamic image structure evolution (DISE) and an DISE-derived single-frame IRSTD framework. First, we resolve IRSTD by a discriminative model without assuming specific background distributions, which is based on a mathematical definition of structure singularity modeling the ideal target structure. Second, to decouple the target distributions from interferences, DISE guides the distorted target to reveal potential structure singularity while suppress the interference signal through iterative procedures of structure collapse, intensity settlement, and collapse convergence. The three functional procedures of DISE perform their own duties regarding target enhancement and background suppression. Moreover, an innovative chain mechanism is introduced to propagate the structure field. Experiments on real data sets demonstrate the superiority of DISE against the state-of-the-art IRSTD methods.

Botnets provide a fundamental infrastructure for various kinds of network attacks, such as DDoS attack, etc. Detecting DDoS attack in botnet is a long-standing challenge. In this paper, we propose a novel method to detect DDoS attack in botnet through the analysis of packet forwarding and network traffic. The primary idea is to collect features from both overall network traffic and packet to describe the attack pattern and conduct a detection model with high accuracy. Firstly, apart from overall network traffic, we calculate several statistics related to looking up functions of flow tables during packet forwarding on switches to describe attack pattern. Secondly, these features are put into a deep learning model to detect DDoS attack. We perform evaluations under Software Defined Networking (SDN) paradigm. In particular, we compare the proposed method with traditional methods. The experimental results demonstrate that the proposed method is meaningful in improving the accuracy of DDoS attack detection by adding packet-level features extracted from packet forwarding.

Mobile-edge computing (or MEC) has aroused a wide attention at the 5th generation mobile networks (5G) communication era. Different edge servers (such as cloudlets, micro data centers, and base stations) have been proposed to support the MEC architecture paradigm. For the diverse loading capacities of different edge servers, computation offloading for the edge services loaded in neighboring edge servers is desired for assuring the overall serve performance as well as the Quality of Experience of users for MEC applications. To dynamically balance the computation load for neighboring edge servers, we model the optimal edge services computation offloading destination determination issue as an optimal transport distances problem in this article. We propose computation offloading via Sinkhorn's matrix scaling (COSIMS) to determine the optimal offloading destination. Experimental evaluations conducted on real-world edge computing data sets indicate that COSIMS can guarantee that the neighboring edge servers cooperatively provide effective services to mobile users with least extra communication hops.

Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSPGoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m + j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.

Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with crucial self-retained spectral information, while also avoiding highly correlated bands to be selected. This article presents a novel approach, referred to as self-mutual information-based band selection (SMI-BS) for hyperspectral image classification (HSIC) to address these two issues. It first constructs a hyperspectral band channel from a hyperspectral image and then takes advantage of such a band channel to coin a new concept of SMI, which is defined as the mutual information (MI) between a selected band, b, and the set of full bands, Ω, I(b; Ω). As a result, a curve plotted as a function of I(b; Ω) versus individual band b, called SMI curve, can be used as a BS criterion which selects those bands with large I(b; Ω) values as desired bands. Since such selected bands may be highly correlated, another new concept, called prominent band (PB), which is defined as a band corresponding to a prominent peak of an SMI curve, is further introduced to avoid selecting highly inter-correlated spectral bands. To validate the utility of SMI-BS in HSIC, experiments are conducted to compare existing state-of-the-art BS methods for performance evaluation. The results demonstrate that SMI-BS is indeed a very effective BS method and also performs better than other test BS methods.

Due to significant geometric distortions and illumination differences, developing techniques for high precision and robust multisource remote sensing image registration poses a great challenge. This article presents an iterative image registration approach, called iterative scale-invariant feature transform (ISIFT) for remote sensing images, which extends the traditional scale-invariant feature transform (SIFT)-based registration system to a close-feedback SIFT system that includes a rectification feedback loop to update rectified parameters in an iterative manner. Its key idea uses consistent feature point sets obtained by maximum similarity to calculate new alignment parameters to rectify the current sensed image and the resulting rectified sensed image is then fed back to update and replace the current sensed image as a new sensed image to reimplement SIFT for next iteration. The same process is repeated iteratively until an automatic stopping rule is satisfied. To evaluate the performance of ISIFT, both the simulated and real images are used for experiments for the validation of ISIFT. In addition, several data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. Furthermore, experiments with different rotation are also performed to verify the adaptability of ISIFT under different rotation distortions. The experimental results demonstrate that ISIFT improves performance and produces better registration accuracy than traditional SIFT-based methods and existing state-of-the-art methods.

A spectral-spatial (SS) hyperspectral classifier generally implements a spectral classifier (SC) followed by a spatial filter (SF) for classification. This article develops a new approach to fusing multiple SC-SF classifiers for hyperspectral image classification (HSIC) as to improve classification performance. To accomplish this goal an iterative process is particularly designed to fuse the spatial-filtered classification maps (SFMaps) produced by each of SC-SF classifiers into one single SFMap via maximum a posteriori (MAP) criterion. Such fused SFMaps are then fed back and added to the current data cube to create a new data set for next round SC-SF classifier fusion. The same process is repeated iteratively until it satisfies an automatic stopping rule. To further fuse more than two SS methods, two approaches are also developed, called simultaneous multiple SC-SF fusion (SMSSF) method and progressive multiple SC-SF fusion (PMSSF) method. Experimental results demonstrate that fusing multiple SC-SF classifiers can indeed perform better than using an individual single SC-SF classifier alone without fusion.

Low rank and sparsity matrix decomposition (LRaSMD) has received considerable interest lately. One of effective methods is called go decomposition (GoDec) which finds low rank and sparse matrices iteratively subject to a predetermined low rank order, m and a sparsity cardinality, k, In order to resolve issue of the empirically determined m and k, the well-known virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) developed in maximum orthogonal complement algorithm (MOCA) are used for this purpose. The constrained energy minimization (CEM) is used for experiments to demonstrate that the GoDec with VD and MX-SVD performs very effectively.

Anomaly detection is an important application in hyperspectral data exploitation. This paper develops a novel fast low rank approximation and sparsity representation approach to anomaly detection. It first uses a standard random projection approach to construct low rank matrix, and then use it to calculate sparsity representation, S. Secondly, continuous to decompose S to get new low rank and sparsity representation until S converges into predefined threshold. Sum all S in the iteration process. Thirdly, using the summation of S to construct original matrix's low rank. Finally, comparing with state-of-art methods like Go Decomposition (GoDec), our method is seven times faster than GoDec's. The experimental on real hyperspectral images results indicate that the detection power of Reed-Xiaoli detector (RXD) is approximately increased 11% based on our sparsity representation comparing with GoDec method but the false alarm is lower than GoDec's.

Salient object detection is usually used as a preprocessing step to facilitate a variety of subsequent applications which should take little time cost. With the quick development of deep learning recently, profound progresses have been made to achieve a new state-of-the-art performance. However, the learned features of the existing deep learning-based methods are not accurate enough thus leading to unsatisfactory detection in complex scenes, such as low contrast or very similar between salient object and background region and multiple (small) salient objects with diverse characteristics. In addition, some post-processing techniques are usually needed for refinement, which is time consuming. To address these issues, this paper presents an efficient fully convolutional salient object detection network. Specifically, we first introduce a visual attention mechanism to guide feature learning in side output layers. In detail, attention weight is employed in a top-down manner which can bridge high level semantic information to help shallow layers better locate salient objects and also filter out noisy response in the background region. Second, we propose a residual refinement network to fuse the learned multilevel features gradually. Not to simply add or concatenate them step by step as previous works, we introduce a second-order term into element-wise addition to learn stage-wise residual features for refinement. Such a second-order term not only benefits efficient gradient propagation but also increases network nonlinearity. Extensive experiments on seven standard benchmarks demonstrate that the proposed approach achieves consistently superior performance and performs well on small salient object detection in comparison with the very recent state-of-the-arts, especially in the metric of structure-measure.

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel reverse attention block to guide side-output residual learning in a top-down manner. Specifically, the current predicted salient regions are erased from each side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in high resolution and accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art methods, and with advantages in terms of simplicity, efficiency and model size.

Urban impervious surface area (ISA) is a key index toward urban eco-system and sustainable urban planning strategy. In this paper, a subpixel approach is proposed to estimate urban ISA values using linear spectral mixture analysis (LSMA)-based hyperspectral imaging techniques. In doing so the concept of virtual dimensionality (VD) is used to first estimate the number of endmembers, then an endmember finding approach is implemented to find VD-determined number of endmembers in a hyperspectral image. Finally, nonnegativity constrained least squares (NCLS) is performed for endmember unmixing. The hyperspectral image used in our approach provides a larger number of spectral dimensions than a multispectral image does so that a sufficient number of endmembers can be found from a hyperspectral image for ISA estimation. What is more, a relationship between ISA values and fractional endmember abundances can be further constructed by linear regression.

This paper proposed a novel local feature descriptor for multisource remote sensing image matching that is robust to significant geometric and illumination differences. In the proposed registration method, traditional SIFT algorithm is applied for local feature extraction and a novel descriptor, named local order pattern based self-similarity descriptor, LOPSS descriptor, is constructed for each extracted feature point. Then, a matching process followed by a reliable outlier removal procedure is implemented for feature matching and mismatch elimination. Finally, registration parameters are estimated by least square method in the affine transformation. The proposed method is applied for matching multisource remote sensing image pairs and the results verify its robustness and discriminability.

High-precision and robust matching of multi-source remote sensing image matching is not easy to achieve because of non-linear intensity differences and significant geometric distortions. A new registration method is proposed by integrating the scale-invariant feature transform (SIFT) and optimization of local self-similarity mutual information (LSS_MI). This method consists of two main steps. In the first step, SIFT approach with a reliable outlier removal procedure is implemented. By repeatedly fine turning several selected matched feature point coordination, a series of registration parameters are estimated by least square method and used to construct initial particle swarms. Then, a local self-similarity descriptor (LSS) is computed for pre-matching image pairs and the optimal match parameters are obtained by optimizing LSS_MI based on QPSO. The experimental results verify that the LSS-MI is more robust and accurate than regional mutual information in multi-source remote sensing image.